System and method of calculating dose uncertainty

ABSTRACT

A dose calculation tool operable to generate a variance map that represents a dose uncertainty. The variance map illustrates on a point-by-point basis where high uncertainty in the dose may exist and where low uncertainty in the dose may exist. The dose uncertainty is a result of an error in one or more data parameters related to a delivery parameter or a computational parameter.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/550,184, filed on Aug. 28, 2009, which is a non-provisionalapplication of and claims priority to U.S. Provisional PatentApplication No. 61/092,523, filed on Aug. 28, 2008. The entire contentsof these applications are incorporated herein by reference.

BACKGROUND OF THE INVENTION

Over the past decades improvements in computers and networking,radiation therapy treatment planning software, and medical imagingmodalities (CT, MRI, US, and PET) have been incorporated into radiationtherapy practice. These improvements have led to the development ofimage guided radiation therapy (“IGRT”). IGRT is radiation therapy thatuses cross-sectional images of the patient's internal anatomy to bettertarget the radiation dose in the tumor while reducing the radiationexposure to healthy organs. The radiation dose delivered to the tumor iscontrolled with intensity modulated radiation therapy (“IMRT”), whichinvolves changing the size, shape, and intensity of the radiation beamto conform to the size, shape, and location of the patient's tumor. IGRTand IMRT lead to improved control of the tumor while simultaneouslyreducing the potential for acute side effects due to irradiation ofhealthy tissue surrounding the tumor.

SUMMARY OF THE INVENTION

IMRT is a state-of-the-art technique for administering radiation tocancer patients. The goal of a treatment is to deliver a prescribedamount of radiation to a tumor, while limiting the amount absorbed bythe surrounding healthy organs. Planning an IMRT treatment requiresdetermining fluence maps, each consisting of hundreds or more beamletintensities.

Several mathematical problems arise in order to optimally administerIMRT. Treatment proceeds by rotating the linac around the patient andcoordinating the leaf movements in the MLC so that the radiationdelivered conforms to some desirable dose distribution at each gantry(beam) angle.

In addition to knowing the beam angles, one must also know how intensethe beams should be at each point (x, y) on the MLC aperture for allgantry angles. These intensity profiles, or fluence maps, arerepresented by two-dimensional, nonnegative functions Ia(x, y) for a=1,2, . . . , k, where k is the number of gantry angles in use. The processof determining the functions Ia(x, y) is often called fluence mapoptimization. Finally, once the fluence maps Ia(x, y) are determined,one must convert these into MLC leaf sequences that attempt to realizethem. The longer an MLC leaf is open at a certain position (x, y), themore dose the tissue along a straight path from that position (plus somesurrounding tissue) absorbs. The process of converting fluence maps intothe opening and closing movements of leaves is called leaf-sequencing.There are many physical and mathematical issues that affect howsuccessful MLC leaf sequences are at approximating the desired fluencemaps.

From a treatment planning perspective, TomoTherapy® treatment technologyallows for tremendous flexibility when treating complicated targetvolumes due to the large number of projections (beam angles) that can beused. The TomoTherapy® system has the ability to deliver radiationhelically to the patient. The unique nature of the helical deliverypattern however, requires the user to specify new planning parameterssuch as field width, pitch, and modulation factor. Failure to selectjudicious values for these parameters may compromise treatment planquality, and may increase the total treatment time, as well as producetreatment plans which are more difficult for the radiation deliverydevice to accurately deliver.

In one embodiment, the invention provides a method of evaluatingdosimetric uncertainties for a radiation delivery. The method includesgenerating a treatment plan for a patient, the treatment plan includinga dose to be delivered to the patient using a radiation delivery device;identifying a data parameter for the radiation delivery device; andgenerating a variance map utilizing a dose calculation module, thevariance map representing an uncertainty indication in the dose to bedelivered to the patient, the uncertainty indication related to the dataparameter.

In another embodiment, the invention provides a method of detecting adelivery error in a radiation delivery device. The method includesgenerating a treatment plan for a patient, the treatment plan includingan intended fluence; delivering radiation to the patient according tothe treatment plan; acquiring output fluence information from theradiation delivery device after delivery of the treatment plan;comparing the output fluence information and the intended fluence todetermine a fluence variance; and applying a dose calculation algorithmto the fluence variance to generate a dose variance map.

In a further embodiment, the invention provides a method of evaluating apartially delivered treatment plan. The method includes generating atreatment plan for a patient, the treatment plan including a pluralityof treatment fractions and intended variance information; delivering atleast one of the treatment fractions to the patient according to thetreatment plan; acquiring output fluence information from a radiationdelivery device after delivery of the treatment fraction; and evaluatingfuture treatment fractions based on a combination of the intendedvariance information and the output fluence information.

Other aspects of the invention will become apparent by consideration ofthe detailed description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a radiation therapy treatment system.

FIG. 2 is a perspective view of a multi-leaf collimator that can be usedin the radiation therapy treatment system illustrated in FIG. 1.

FIG. 3 is a schematic illustration of the radiation therapy treatmentsystem of FIG. 1.

FIG. 4 is a schematic diagram of a software program used in theradiation therapy treatment system.

FIG. 5 illustrates dose volume histograms for a representative patientreplanned using an increased pitch.

FIG. 6 illustrates histograms of the normalized leaf open-times bothbefore and after replanning.

FIG. 7 illustrates slices of variance maps taken between thereconstructed and planned DQA dose grids for both the low and high pitchplans.

FIG. 8 illustrates the results of ion-chamber measurements made bothbefore and after replanning, and indicates a reduction in error with thehigh pitch plans.

DETAILED DESCRIPTION OF THE INVENTION

Before any embodiments of the invention are explained in detail, it isto be understood that the invention is not limited in its application tothe details of construction and the arrangement of components set forthin the following description or illustrated in the following drawings.The invention is capable of other embodiments and of being practiced orof being carried out in various ways. Also, it is to be understood thatthe phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items. Unless specified or limited otherwise, theterms “mounted,” “connected,” “supported,” and “coupled” and variationsthereof are used broadly and encompass both direct and indirectmountings, connections, supports, and couplings.

Although directional references, such as upper, lower, downward, upward,rearward, bottom, front, rear, etc., may be made herein in describingthe drawings, these references are made relative to the drawings (asnormally viewed) for convenience. These directions are not intended tobe taken literally or limit the present invention in any form. Inaddition, terms such as “first,” “second,” and “third” are used hereinfor purposes of description and are not intended to indicate or implyrelative importance or significance.

In addition, it should be understood that embodiments of the inventioninclude both hardware, software, and electronic components or modulesthat, for purposes of discussion, may be illustrated and described as ifthe majority of the components were implemented solely in hardware.However, one of ordinary skill in the art, and based on a reading ofthis detailed description, would recognize that, in at least oneembodiment, the electronic based aspects of the invention may beimplemented in software. As such, it should be noted that a plurality ofhardware and software based devices, as well as a plurality of differentstructural components may be utilized to implement the invention.Furthermore, and as described in subsequent paragraphs, the specificmechanical configurations illustrated in the drawings are intended toexemplify embodiments of the invention and that other alternativemechanical configurations are possible.

FIG. 1 illustrates a radiation therapy treatment system 10 that canprovide radiation therapy to a patient 14. The radiation therapytreatment can include photon-based radiation therapy, brachytherapy,electron beam therapy, proton, neutron, or particle therapy, or othertypes of treatment therapy. The radiation therapy treatment system 10includes a gantry 18. The gantry 18 can support a radiation module 22,which can include a radiation source 24 and a linear accelerator 26operable to generate a beam 30 of radiation. Though the gantry 18 shownin the drawings is a ring gantry, i.e., it extends through a full 360°arc to create a complete ring or circle, other types of mountingarrangements may also be employed. For example, a non-ring-shapedgantry, such as a C-type, partial ring gantry, or robotic arm could beused. Any other framework capable of positioning the radiation module 22at various rotational and/or axial positions relative to the patient 14may also be employed. In addition, the radiation source 24 may travel inpath that does not follow the shape of the gantry 18. For example, theradiation source 24 may travel in a non-circular path even though theillustrated gantry 18 is generally circular-shaped.

The radiation module 22 can also include a modulation device 34 operableto modify or modulate the radiation beam 30. The modulation device 34provides the modulation of the radiation beam 30 and directs theradiation beam 30 toward the patient 14. Specifically, the radiationbeam 34 is directed toward a portion of the patient. Broadly speaking,the portion may include the entire body, but is generally smaller thanthe entire body and can be defined by a two-dimensional area and/or athree-dimensional volume. A portion desired to receive the radiation,which may be referred to as a target 38 or target region, is an exampleof a region of interest. Another type of region of interest is a regionat risk. If a portion includes a region at risk, the radiation beam ispreferably diverted from the region at risk. The patient 14 may havemore than one target region that needs to receive radiation therapy.Such modulation is sometimes referred to as intensity modulatedradiation therapy (“IMRT”).

The modulation device 34 can include a collimation device 42 asillustrated in FIG. 2. The collimation device 42 includes a set of jaws46 that define and adjust the size of an aperture 50 through which theradiation beam 30 may pass. The jaws 46 include an upper jaw 54 and alower jaw 58. The upper jaw 54 and the lower jaw 58 are moveable toadjust the size of the aperture 50.

In one embodiment, and illustrated in FIG. 2, the modulation device 34can comprise a multi-leaf collimator 62, which includes a plurality ofinterlaced leaves 66 operable to move from position to position, toprovide intensity modulation. It is also noted that the leaves 66 can bemoved to a position anywhere between a minimally and maximally-openposition. The plurality of interlaced leaves 66 modulate the strength,size, and shape of the radiation beam 30 before the radiation beam 30reaches the target 38 on the patient 14. Each of the leaves 66 isindependently controlled by an actuator 70, such as a motor or an airvalve so that the leaf 66 can open and close quickly to permit or blockthe passage of radiation. The actuators 70 can be controlled by acomputer 74 and/or controller.

The radiation therapy treatment system 10 can also include a detector78, e.g., a kilovoltage or a megavoltage detector, operable to receivethe radiation beam 30. The linear accelerator 26 and the detector 78 canalso operate as a computed tomography (CT) system to generate CT imagesof the patient 14. The linear accelerator 26 emits the radiation beam 30toward the target 38 in the patient 14. The target 38 absorbs some ofthe radiation. The detector 78 detects or measures the amount ofradiation absorbed by the target 38. The detector 78 collects theabsorption data from different angles as the linear accelerator 26rotates around and emits radiation toward the patient 14. The collectedabsorption data is transmitted to the computer 74 to process theabsorption data and to generate images of the patient's body tissues andorgans. The images can also illustrate bone, soft tissues, and bloodvessels.

The CT images can be acquired with a radiation beam 30 that has afan-shaped geometry, a multi-slice geometry or a cone-beam geometry. Inaddition, the CT images can be acquired with the linear accelerator 26delivering megavoltage energies or kilovoltage energies. It is alsonoted that the acquired CT images can be registered with previouslyacquired CT images (from the radiation therapy treatment system 10 orother image acquisition devices, such as other CT scanners, MRI systems,and PET systems). For example, the previously acquired CT images for thepatient 14 can include identified targets 38 made through a contouringprocess. The newly acquired CT images for the patient 14 can beregistered with the previously acquired CT images to assist inidentifying the targets 38 in the new CT images. The registrationprocess can use rigid or deformable registration tools.

The image data can be presented on a video display as either athree-dimensional image or a series of two-dimensional images. Inaddition, the image data comprising the images can be either voxels (forthree-dimensional images) or pixels (for two-dimensional images). Theterm image element is used generally in the description to refer toboth.

In some embodiments, the radiation therapy treatment system 10 caninclude an x-ray source and a CT image detector. The x-ray source andthe CT image detector operate in a similar manner as the linearaccelerator 26 and the detector 78 as described above to acquire imagedata. The image data is transmitted to the computer 74 where it isprocessed to generate images of the patient's body tissues and organs.

The radiation therapy treatment system 10 can also include a patientsupport, such as a couch 82 (illustrated in FIG. 1), which supports thepatient 14. The couch 82 moves along at least one axis 84 in the x, y,or z directions. In other embodiments of the invention, the patientsupport can be a device that is adapted to support any portion of thepatient's body. The patient support is not limited to having to supportthe entire patient's body. The system 10 also can include a drive system86 operable to manipulate the position of the couch 82. The drive system86 can be controlled by the computer 74.

The computer 74, illustrated in FIGS. 2 and 3, includes an operatingsystem for running various software programs and/or a communicationsapplication. In particular, the computer 74 can include a softwareprogram(s) 90 that operates to communicate with the radiation therapytreatment system 10. The computer 74 can include any suitableinput/output device adapted to be accessed by medical personnel. Thecomputer 74 can include typical hardware such as a processor, I/Ointerfaces, and storage devices or memory. The computer 74 can alsoinclude input devices such as a keyboard and a mouse. The computer 74can further include standard output devices, such as a monitor. Inaddition, the computer 74 can include peripherals, such as a printer anda scanner.

The computer 74 can be networked with other computers 74 and radiationtherapy treatment systems 10. The other computers 74 may includeadditional and/or different computer programs and software and are notrequired to be identical to the computer 74, described herein. Thecomputers 74 and radiation therapy treatment system 10 can communicatewith a network 94. The computers 74 and radiation therapy treatmentsystems 10 can also communicate with a database(s) 98 and a server(s)102. It is noted that the software program(s) 90 could also reside onthe server(s) 102.

The network 94 can be built according to any networking technology ortopology or combinations of technologies and topologies and can includemultiple sub-networks. Connections between the computers and systemsshown in FIG. 3 can be made through local area networks (“LANs”), widearea networks (“WANs”), public switched telephone networks (“PSTNs”),wireless networks, Intranets, the Internet, or any other suitablenetworks. In a hospital or medical care facility, communication betweenthe computers and systems shown in FIG. 3 can be made through the HealthLevel Seven (“HL7”) protocol or other protocols with any version and/orother required protocol. HL7 is a standard protocol which specifies theimplementation of interfaces between two computer applications (senderand receiver) from different vendors for electronic data exchange inhealth care environments. HL7 can allow health care institutions toexchange key sets of data from different application systems.Specifically, HL7 can define the data to be exchanged, the timing of theinterchange, and the communication of errors to the application. Theformats are generally generic in nature and can be configured to meetthe needs of the applications involved.

Communication between the computers and systems shown in FIG. 3 can alsooccur through the Digital Imaging and Communications in Medicine (DICOM)protocol with any version and/or other required protocol. DICOM is aninternational communications standard developed by NEMA that defines theformat used to transfer medical image-related data between differentpieces of medical equipment. DICOM RT refers to the standards that arespecific to radiation therapy data.

The two-way arrows in FIG. 3 generally represent two-way communicationand information transfer between the network 94 and any one of thecomputers 74 and the systems 10 shown in FIG. 3. However, for somemedical and computerized equipment, only one-way communication andinformation transfer may be necessary.

The software program 90 includes a plurality of modules that communicatewith one another to perform functions of the radiation therapy treatmentprocess. The various modules communication with one another to determineif delivery of the radiation therapy treatment plan occurred asintended.

The software program 90 includes a treatment plan module 106 operable togenerate a treatment plan for the patient 14 based on data input to thesystem 10 by medical personnel. The data can include one or more images(e.g., planning images and/or pre-treatment images) of at least aportion of the patient 14 and a fluence map. The treatment plan module106 separates the treatment into a plurality of fractions and determinesthe radiation dose for each fraction or treatment based on theprescription input by medical personnel. The treatment plan module 106also determines the radiation dose for the target 38 based on variouscontours drawn around the target 38. Multiple targets 38 may be presentand included in the same treatment plan.

The software program 90 also includes a patient positioning module 110operable to position and align the patient 14 with respect to theisocenter of the gantry 18 for a particular treatment fraction. Whilethe patient is on the couch 82, the patient positioning module 110acquires an image of the patient 14 and compares the current position ofthe patient 14 to the position of the patient in a reference image. Thereference image can be a planning image, any pre-treatment image, or acombination of a planning image and a pre-treatment image. If thepatient's position needs to be adjusted, the patient positioning module110 provides instructions to the drive system 86 to move the couch 82 orthe patient 14 can be manually moved to the new position. In oneconstruction, the patient positioning module 110 can receive data fromlasers positioned in the treatment room to provide patient position datawith respect to the isocenter of the gantry 18. Based on the data fromthe lasers, the patient positioning module 110 provides instructions tothe drive system 86, which moves the couch 82 to achieve properalignment of the patient 14 with respect to the gantry 18. It is notedthat devices and systems, other than lasers, can be used to provide datato the patient positioning module 110 to assist in the alignmentprocess.

The patient positioning module 110 also is operable to detect and/ormonitor patient motion during treatment. The patient positioning module110 may communicate with and/or incorporate a motion detection system112, such as x-ray, in-room CT, laser positioning devices, camerasystems, spirometers, ultrasound, tensile measurements, chest bands, andthe like. The patient motion can be irregular or unexpected, and doesnot need to follow a smooth or reproducible path.

The software program 90 also includes a treatment delivery module 114operable to instruct the radiation therapy treatment system 10 todeliver the treatment plan to the patient 14 according to the treatmentplan. The treatment delivery module 114 can generate and transmitinstructions to the gantry 18, the linear accelerator 26, the modulationdevice 34, and the drive system 86 to deliver radiation to the patient14. The instructions coordinate the necessary movements of the gantry18, the modulation device 34, and the drive system 86 according to afluence map to deliver the radiation beam 30 to the proper target in theproper amount as specified in the treatment plan.

The treatment delivery module 114 also calculates the appropriatepattern, position, and intensity of the radiation beam 30 to bedelivered, to match the prescription as specified by the treatment plan.The pattern of the radiation beam 30 is generated by the modulationdevice 34, and more particularly by movement of the plurality of leavesin the multi-leaf collimator. The treatment delivery module 114 canutilize canonical, predetermined or template leaf patterns to generatethe appropriate pattern for the radiation beam 30 based on the treatmentparameters. The treatment delivery module 114 can also include a libraryof patterns for typical cases that can be accessed in which to comparethe present patient data to determine the pattern for the radiation beam30.

The software program 90 also includes a feedback module 118 operable toreceive data from the radiation therapy treatment system 10 during apatient treatment. The feedback module 118 can receive data from theradiation therapy treatment device and can include information relatedto patient transmission data, ion chamber data, fluence output data, MLCdata, system temperatures, component speeds and/or positions, flowrates, etc. The feedback module 118 can also receive data related to thetreatment parameters, amount of radiation dose the patient received,image data acquired during the treatment, and patient movement. Inaddition, the feedback module 118 can receive input data from a userand/or other sources. The feedback module 118 acquires and stores thedata until needed for further processing.

The software program 90 also includes an analysis module 122 operable toanalyze the data from the feedback module 118 to determine whetherdelivery of the treatment plan occurred as intended and to validate thatthe planned delivery is reasonable based on the newly-acquired data. Theanalysis module 122 can also determine, based on the received dataand/or additional inputted data, whether a problem has occurred duringdelivery of the treatment plan. For example, the analysis module 122 candetermine if the problem is related to an error of the radiation therapytreatment device 10, an anatomical error, such as patient movement,and/or a clinical error, such as a data input error.

The analysis module 122 can detect errors in the radiation therapytreatment device 10 related to the couch 82, the device output, thegantry 18, the multi-leaf collimator 62, the patient setup, and timingerrors between the components of the radiation therapy treatment device10. For example, the analysis module 122 can determine if a couchreplacement was performed during planning, if fixation devices wereproperly used and accounted for during planning, if position and speedis correct during treatment.

The analysis module 122 can determine whether changes or variationsoccurred in the output parameters of the radiation therapy treatmentdevice 10. With respect to the gantry 18, the analysis module 122 candetermine if there are errors in the speed and positioning of the gantry18. The analysis module 122 can receive data to determine if themulti-leaf collimator 62 is operating properly. For example, theanalysis module 122 can determine if the leaves 66 move at the correcttimes, if any leaves 66 are stuck in place, if leaf timing is properlycalibrated, and whether the leaf modulation pattern is correct for anygiven treatment plan. The analysis module 122 also can validate patientsetup, orientation, and position for any given treatment plan. Theanalysis module 122 also can validate that the timing between the gantry18, the couch 62, the linear accelerator 26, the leaves 66 are correct.

The software program 90 also includes a dose calculation module 126operable to generate a variance map that represents a dose uncertainty.The dose calculation module 126 receives a density image (e.g., apatient CT image), the relative positions and motions of the radiationsource (“source”) with respect to the density image (position and motionis referred to as “plan geometry”), and a fluence map describing thefluence incident to a 2-D plane in front of the source at each moment intime. From these inputs (and others, such as machine commissioning) adose image is calculated. The fluence map over time is replaced by a mapof fluence uncertainty, error, or other metric. The dose calculationmodule 126 is run as usual, using the fluence uncertainty/error map likeit would normally use the fluence map. The resulting image representsthe uncertainty/error projected into image space, instead of a doseimage.

For example, suppose we have a fluence map for a radiation treatmentplan and a corresponding fluence map reconstructed after one delivery ofthat plan. The dose calculation module 126 generates a variance map byreplacing the fluence map with the square of the difference between theplanned and delivered fluence maps. The dose calculation module 126 isrun using this variance map. The resulting image represents the variancein the dose to each image voxel, accumulated over the duration of thedelivery. The square root of these variance values would be the standarddeviation of delivered dose versus planned dose to each voxel over theduration of the delivery.

Uncertainty or error in plan geometry at a particular time can berepresented as uncertainty in the fluence map over a neighboring timeinterval. For example, if there is uncertainty in the gantry position atsome point in time (t), and the gantry is moving, then the fluenceuncertainty for a nearby points in time (t′) is affected by theprobability that the gantry is at the position expected at that time(t′).

There is a noted difference between projecting uncertainty/variance fromthe fluence map to image space versus comparing two dose images:multiple errors in a fluence map may cancel out in a normal dosecalculation, so the error may not show up in the computed dose volume.But multiple errors projected through the dose calculation module 126 asvariance (or a similar non-negative metric) are accumulated and do notcancel out. So, regions of dose uncertainty will be visible in thecomputed uncertainty volume.

The variance map generated by the dose calculation module 126illustrates on a point-by-point basis where high uncertainty in the dosemay exist and where low uncertainty in the dose may exist. The doseuncertainty is a result of an error in one or more data parametersrelated to a delivery parameter or a computational parameter. In otherwords, the dose uncertainty represents the effect of uncertainties inthe planning and delivery of radiation to the patient. The doseuncertainty can be taken into consideration prospectively when planninga treatment plan for the patient and retrospectively to adjust or modifythe treatment plan.

The dose uncertainty serves as a constraint when determining a settingof the data parameter(s). The data parameters related to delivery ofradiation can include linac output, leaf timing, jaw position, spectralchanges in attenuation/energy components of a radiation beam, couchposition, and gantry position. Other data parameters related to modelingeffects can include leaf size, leaf shape, jaw shape, tongue and grooveinformation of two adjacent leaves, a source-to-axis distance, and achange in beam shape. The dose calculation module 126 can generate adose variance map reflecting the effect on dose for a single dataparameter. The dose calculation module 126 also can generate a dosevariance map reflecting the effect on dose for a combination of dataparameters. Because the variance is additive (var[total]=var[a]+var[b]+. . . ), the dose calculation module 126 can combine (sum) the variancein fluence caused by many data parameters and generate a dose variancemap.

The dose calculation module 126 allows the user to anticipate operationof the radiation delivery device and to incorporate device feedback,which includes the error(s) to modify the treatment plan. The dosecalculation module 126 proactively accounts for mechanical variations ofthe device and how they may impact the treatment delivery.

Accordingly, the dose calculation module 126 can optimize a treatmentplan to reduce the dose uncertainty by using the uncertainty indicationto set one or more of the data parameters when preparing for treatmentdelivery. More specifically, the treatment plan can be optimized toreduce the uncertainty in dose for a particular area in the patient asshown on the dose variance map. In addition, the treatment plan can bemodified to account for the dose variance map, a different radiationdelivery device can be selected to deliver the treatment, and anentirely different treatment plan can be generated or chosen from a listof alternate plans previously generated.

The dose calculation module 126 also can optimize the treatment plan toreduce dose uncertainty by reducing the treatment plan's dependence onMLC leaves that have higher uncertainty (e.g., if a leaf is starting togo fall out of tolerance, we can generate treatment plans that use itless often), adjusting the treatment plan isocenter to reduce the impactof MLC or jaw positional uncertainties, adjusting the fraction of leafopen time in projections to reduce leaf timing uncertainties—leaf timinguncertainties are larger when a leaf has a very short open time or anopen time nearly as long as the projection time, and/or reducing thetreatment plan's dependence on gantry angles that have higher machineoutput uncertainty or shoot through regions of space that are affectedby couch top position uncertainty.

The dose calculation module 126 also can evaluate the deliverability ofan optimized treatment plan. For example, the dose calculation module126 can recommend that a different radiation delivery device be used todeliver the treatment plan to the patient. The dose calculation module126 also can generate an alert to the user when the dose variance mapindicates that the uncertainty in dose exceeds a predeterminedthreshold. The alert can be the basis for a number of decisions by theuser, including selecting a different plan, reoptimizing the currentplan, adjusting patient position or machine parameters, or performingrepairs or maintenance on the delivery device.

In a retrospective analysis, the dose calculator 126 can detect adelivery error in the radiation delivery device. To do so, the dosecalculator 126 can receive exit data from the radiation delivery deviceafter delivery of the treatment plan. The exit data (e.g., outputfluence information) can come from a detector such as, for example, asingle-row gas ionization detector (e.g., xenon), a multi-row gasionization detector, a crystal detector, a solid state detector, a flatpanel detector (e.g., Amorphous silicon or selenium), or other suitabledetecting devices. The dose calculator 126 can compare an intendedfluence, which was specified in the treatment plan, with the outputfluence information to generate a fluence variance. The dose calculator126 uses the fluence variance as an input to a dose calculationalgorithm to generate a dose variance map. The dose variance map can bedisplayed to the user.

The fluence variance can be based on feedback from the radiationdelivery device over the course of a plurality of treatment fractions.Based on the fluence variance, the treatment plan can be modified, a newtreatment plan can be generated, and/or a different radiation deliverydevice may be selected to deliver the remaining treatment fractions.

The dose calculation module 126 also can evaluate a partially deliveredtreatment plan to determine how pre-treatment variance risk is actuallyrealized by looking at actual variances. A treatment plan is generatedthat includes a plurality of treatment fractions and intended varianceinformation. After delivery of at least one of the treatment fractions,the dose calculation module 126 acquires output fluence information fromthe radiation delivery device. The dose calculation module 126 canevaluate future treatment fractions and assess the risk of treatmentplan deviation for future treatments based upon a combination of theintended variance information and the measured variance information.Based on that risk, the user can decide whether to proceed with theplan, whether to reoptimize the plan, whether to choose a different planto deliver, or whether other delivery options exist.

EXAMPLE

Purpose: To investigate the source of delivery quality assurance (DQA)errors observed for a subset of patients planned for treatment onTomoTherapy® treatment systems.

Method and Materials: Six patients planned on TomoTherapy® systems wereselected for analysis. Three patients had passing DQA plans and threehad DQA plans with ion-chamber measurements that deviated from theexpected dose by more than 3%. The patients were planned using similarparameters, including a 2.5 cm field width and pitch values ranging from0.143-0.215. Machine output was determined not to be a problem sonormalized leaf timing sinograms were analyzed to determine the meanleaf open-time for each plan. This analysis suggests the observeddiscrepancies are associated with plans having predominantly low LOTs.To test this, patients with out of tolerance DQA measurements werereplanned using an increased pitch of 0.287. After replanning, new DQAplans were generated and ion-chamber measurements performed. Exitfluence data was also collected during the DQA delivery using theonboard MVCT detectors for dose reconstruction purposes.

Results: Sinogram analysis showed increases in mean leaf open-times of30-85% for the higher pitch plans. In addition, ion-chamber measurementsshowed a reduction in point dose errors of 1.9-4.4%, bringing thepatient plans within the ±3% acceptance criteria. Dose reconstructionresults were in excellent agreement with ion-chamber measurements andclearly illustrate the impact of leaf timing errors on plans havingpredominantly small leaf open-times.

Conclusion: The impact of leaf timing errors on treatment plans with lowmean leaf open-times can be significant. This becomes important fortreatment plans using low pitches, or potentially for hyperfractionatedtreatment schedules. The ability to reduce the impact of these deliveryerrors by increasing the treatment plan pitch is demonstrated. Inaddition, the efficacy of dose reconstruction in diagnosing deliveryerrors is established.

Discussion: This work arose out of a clinical situation where a subsetof patients being planned for treatment on TomoTherapy® systems hadpatient specific delivery QA (DQA) point dose measurements that failedto meet the ±3% acceptance criteria set by our institution. Typicalsources of DQA error such as phantom misalignment and machine outputvariation were eliminated from the list of possible causes by utilizingonboard MVCT imaging for setup verification, and by alternatingmeasurements of failing DQA plans with passing plans having similar planparameters—always observing the same result with a near constant doserate.

To diagnose this issue, six patients planned for treatment onTomoTherapy® treatment systems were selected for analysis: three withplans that passed DQA and three with ion-chamber measurements thatdeviated from the expected values by more than 3%. All plans had similarparameters including a 2.5 cm field width and 15 sec gantry period.Pitch values—defined as the couch distance traveled per gantry rotationdivided by the field width, were also similar and ranged from0.143-0.215. For each patient plan, normalized leaf timingsinograms—which contain information about the amount of time each leafof the MLC is open relative to the total projection time, were obtainedfrom the treatment planning system and read into MATLAB for analysis.Mean fractional leaf open-times were computed and are listed in Table 1along with other treatment plan parameters and DQA point dose results.

As seen from Table 1, the out of tolerance DQA treatment plans haveconsiderably lower mean leaf open-times. Based on this result it washypothesized that increasing the mean leaf open time for patients 1-3 inTable 1 would result in a reduction in the error seen in the DQAmeasurements. To test this hypothesis, patients 1-3 were replanned withan increased pitch of 0.287. Increasing the pitch effectively increasesthe mean leaf open-time by forcing the same prescription dose to bedelivered in fewer rotations. After replanning, new DQA treatment planswere generated and ion-chamber measurements were performed. In additionto DQA ion-chamber measurements, exit fluence data was also collectedusing the onboard MVCT detectors during the delivery of both theoriginal and replanned DQA procedures. Using tools developed byTomoTherapy Inc., this data was utilized to reconstruct deliveredfluence sinograms by looking at the signal profile of the individualdetector channels taken over the time of each projection. Thesesinograms were then used to reconstruct the delivered dose in the DQAphantom image.

TABLE 1 Patient plan parameters and DQA measurement data Plan parametersPatient data Mean DQA dose errors Disease Dose/fx fractional PlannedDiscrepancy Patient site (Gy) Pitch leaf time dose (Gy) (%) 1 Head and2.12 0.143 0.196 1.589 4.47 neck 2 Thorax 1.80 0.215 0.304 1.448 3.59 3Thyroid 2.00 0.215 0.285 1.391 4.96 4 Lung 3.00 0.215 0.519 2.651 −0.535 Prostate 2.50 0.172 0.448 2.877 −0.48 6 Pelvis 3.00 0.215 0.472 2.487−0.16

FIG. 5 shows dose volume histograms for a representative patientreplanned using an increased pitch of 0.287, while FIG. 6 showshistograms of the normalized leaf open times both before and afterreplanning. These figures illustrate that while near equivalent plansare achieved using the two different pitch values, the mean leaf opentime is increased by a factor of 1.85 for the increased pitch plan. FIG.7 shows slices of difference maps taken between the reconstructed andplanned DQA dose grids for both the low and high pitch plans. Thesedifference maps are taken in the plane of the DQA ion-chambermeasurements and illustrate the impact of leaf timing errors whentreating plans that use predominantly low leaf open-times. This resultis likely due to the greater importance of individual leaf latencyerrors when the total leaf open time is small, as well as the non-linearbehavior of the MLC leaves when operating at very short leaf open times.FIG. 8 shows the results of ion-chamber measurements made both beforeand after replanning, and indicates a reduction in error with the highpitch plans ranging from 1.9-4.4% for the three patients examined.Reconstructed dose values are also included in this figure and showexcellent agreement with measured values for all plans delivered.

The example presented above tells us that for the radiation therapyplans described therein, there was an error in the fluence mapidentified that is not related to the plan itself. In one particularcase, it is identified as a leaf open time error. As discussed in moredetail below, one aspect of the invention includes a method oftransferring that fluence uncertainty into something visual. In essence,the invention includes a method of using dose calculation as a means tovisualize uncertainty (i.e., visualize errors in the fluence map bytransforming those errors into errors in dose). We can take a collectionof error sources (such as MLC errors, linac output variation, deliveryuncertainties, gantry motion, beam trajectory through the patient, couchmotion, IVDT/density uncertainties, machine calibration parameters,etc.) and use the dose calculator to combine the errors like dose tomake an error or variance map. Leaf open time, as discussed herein, isone possible error that can be identified and calculated according tothis method.

In one embodiment, the method includes replacing the fluence map with anuncertainty (error or variance) map and sending the uncertaintyindication through the dose calculator to get a dose uncertainty. Inthis way, the variance is distributed in real space (ray tracing fromsinogram space into patient space). Then, a convolution algorithm can beapplied to the variance and in some embodiments, optimize the plan withrespect to the uncertainty indication.

In one particular example, a treatment plan's sensitivity to delivery ormodeling errors in the fluence map can be evaluated by generating avariance (or error) map post-delivery throughout the treatment volume.In principle, this approach can be usable for investigating any type ofdelivery or modeling error that can be estimated on a per-beamlet basis.One type of potential delivery error is related to a leaf open-timeparameter, which is discussed below.

Certain treatment plans can be sensitive to short leaf open-time errors.In one example, one leaf was consistently about 6 ms “hot”. A leafopen-time error generally manifests itself as a dose error for treatmentplans with predominantly short leaf open-times using (in this case) the“hot” leaf. However, it is not necessarily the case that all treatmentplans with a significant number of short leaf open-time beamlets willhave dose errors. For example, if the short leaf open-time beamlets (andtheir associated errors) are distributed throughout the dose volume theeffect in any one region may be negligible. Conversely, a treatment planwith relatively small number of short leaf open-time beamlets might showa significant dose error if many of those beamlets dominate the dose inone location.

In order to evaluate a treatment plan for sensitivity to delivery errors(e.g., short leaf open-times) it is necessary to generate a map ofdelivery error throughout the treatment volume. This is substantiallysimilar to the computation that the dose calculator does. If wesubstitute an estimate of delivery error (variance) for fluence at eachbeamlet then the dose calculator will generate a variance map. Otherchanges in the parameters supplied to the dose calculator will berequired (e.g., elimination of adjacent-leaf corrections), butpreliminary discussions turned up no significant implementation issues.

The most accurate estimate of dose error would come from amachine-specific leaf open-time error measurement. However, since leafbehavior may change over time it would probably be best to use a generic“worst-case” estimate of leaf open-time error to identify treatmentplans that could be problematic in general.

Note that the units of variance supplied to the dose calculator shouldbe directly proportional to fluence so the contributions from multiplebeamlets can sum like the dose would. In the case of leaf open-timeerrors, that would be time (instead of the unit-less percent LOT error,for example). If a percent-error is desired, that calculation can occurafter the error is summed.

Various features and advantages of the invention are set forth in thefollowing claims.

What is claimed is:
 1. A method of detecting a delivery error in aradiation delivery device, the method comprising using a computer tocarry out the steps of: generating a treatment plan for a patient, thetreatment plan including an intended fluence; delivering radiation tothe patient according to the treatment plan; acquiring output fluenceinformation from the radiation delivery device after delivery of thetreatment plan; comparing the output fluence information and theintended fluence to determine a fluence variance; and applying a dosecalculation algorithm to the fluence variance to generate a dosevariance map.
 2. The method of claim 1 wherein the fluence variance isbased on feedback from the radiation delivery device over the course ofa plurality of treatments.
 3. The method of claim 1 further comprisingmodifying the treatment plan based on the fluence variance.
 4. Themethod of claim 1 further comprising generating a new treatment planbased on the fluence variance.
 5. The method of claim 1 furthercomprising selecting a different radiation delivery device to deliverthe treatment plan based on the fluence variance.
 6. The method of claim1 further comprising modifying the treatment plan based on anticipatedchanges to the radiation delivery device.
 7. The method of claim 1further comprising displaying the dose variance map.
 8. The method ofclaim 1 wherein the dose variance map represents an uncertaintyindication in the dose to be delivered to the patient, the uncertaintyindication related to a data parameter of the radiation delivery device.9. The method of claim 8 wherein the data parameter includes acombination of multiple single data parameters.
 10. The method of claim8 wherein the data parameter is fluence.
 11. The method of claim 8wherein the data parameter is linac output.
 12. The method of claim 8wherein the data parameter is leaf timing.
 13. The method of claim 8wherein the data parameter is jaw position.
 14. The method of claim 8wherein the data parameter is couch position.
 15. The method of claim 8wherein the data parameter is gantry position.